CryoDECO: disentangling compositional and conformational heterogeneity in cryo-EM with foundation-model priors
Abstract
Cryo-electron microscopy has transformed macromolecular structure determination, but heterogeneous biological samples contain multiple complexes and conformational states that remain difficult to resolve. This difficulty reflects a circular dependency among particle separation, pose estimation and structure reconstruction. We developed CryoDECO, a prior-guided framework that enters this coupled inference problem from structural representations learned from large-scale cryo-EM data rather than from random initialization, and that reduces manual tuning. CryoDECO separates simulated mixtures of up to 100 structures, resolves experimental and native-derived mixtures into populations suitable for high-resolution refinement, and captures continuous motion together with local density differences within a shared trajectory.
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